News Aggregator

Securing the Model Context Protocol (MCP): New AI Security Risks in Agentic Workflows

Aggregated on: 2025-10-02 19:21:18

The Model Context Protocol (MCP), introduced in late 2024, is a significant move forward towards transforming the agentic AI revolution by providing a mechanism for them to connect with enterprise tools, APIs, and databases. The protocol presents a standardized way for large language models (LLMs) and business workflows to communicate with business systems, databases, APIs, and even development environments. Just as Open Database Connectivity (ODBC) standardized access to databases, MCP offers a standard way for AI agents to interact with data and applications across an enterprise. However, as MCP is adopted across organizations, we are also seeing the introduction of new types of security risks that did not exist before. The same abilities that make MCP so powerful, such as bidirectional communication, agentic features, tool descriptions, etc., all introduce a new threat landscape that cybersecurity professionals may not be ready for.

Testing Updates in Insert-Only Ledger Tables and Understanding Updates in Updatable Ledger Tables

Aggregated on: 2025-10-02 18:21:18

In SQL Server, ledger tables offer powerful tamper-evident functionality, which is essential for systems that require high levels of trust and auditability. Two distinct types serve different needs: insert-only ledger tables and updatable ledger tables. Insert-only tables enforce strict immutability, allowing data to be added but never altered or deleted, making them ideal for transaction logs or event sourcing. Conversely, updatable ledger tables permit modifications and deletions while meticulously maintaining a cryptographically verifiable history of all changes, much like a blockchain. This article provides a hands-on demonstration of these principles. We will test update operations against insert-only tables to confirm their constraints and then explore how updates are seamlessly and transparently managed in updatable ledger tables, complete with practical examples.

AI Infrastructure Guide: Tools, Frameworks, and Architecture Flows

Aggregated on: 2025-10-02 17:21:21

Building robust AI infrastructure requires understanding both the theoretical foundations and practical implementation details across multiple layers of technology. This comprehensive guide provides the definitive resource for architecting, deploying, and managing AI systems at any scale — from experimental prototypes to enterprise-grade production deployments serving millions of users. Modern AI applications demand sophisticated infrastructure that can handle the computational intensity of large language models, the complexity of multi-agent systems, and the real-time requirements of interactive applications. The challenge lies not just in selecting the right tools, but in understanding how they integrate across the entire technology stack to deliver reliable, scalable, and cost-effective solutions.

Building ML Platforms for Real-Time Integrity

Aggregated on: 2025-10-02 16:21:18

Large-scale social networks face a universal challenge: maintaining safe and reliable environments as user traffic grows exponentially. Manual processes often break under load, while ad-hoc machine learning models frequently fail to generalize. This article explores how a large-scale platform could address the challenge by developing a comprehensive machine learning infrastructure. Single filters or stand-alone models rarely survive long at scale.

Building a Scalable and Reliable Marketing Data Stack on GCP

Aggregated on: 2025-10-02 15:21:18

Creative campaigns are no longer modern marketing; data is. And not any data: clean, contextual, and timely data that fuels specific, personalised experiences that enable quantifiable outcomes. If you have dozens (or hundreds) of campaigns running across platforms such as Google Ads, Meta, and programmatic DSPs, the infrastructure that enables this orchestration is just as important as the insights themselves. Today, scaling a marketing data stack is not about selecting a BI tool or lifting a few datasets to the cloud. It’s about architecting a resilient foundation on GCP that can ingest campaign data from multiple sources, support time-zone distributed teams, and power real-time dashboards, all while maintaining governance and operational reliability.

Infrastructure as Code (IaC) in a Multi-Cloud Environment: Consistency and Security Issues

Aggregated on: 2025-10-02 14:21:18

Relevance of the Study Modern organizations are increasingly turning to cloud technologies to improve the flexibility, scalability, and efficiency of their IT infrastructure. One important tool in this process is Infrastructure as Code (IaC), which allows organizations to describe their infrastructure using code, automate the deployment process, reduce the risk of human error, and ensure consistency across different stages of the application lifecycle. In addition, there is a trend towards multi-cloud architectures, where companies use multiple cloud providers to spread the load, improve fault tolerance, and comply with security and data localization regulations. This approach allows organizations to take advantage of the benefits of different cloud providers while minimizing the risks associated with a single provider.

Unpack IPTables: Its Inner Workings With Commands and Demos

Aggregated on: 2025-10-02 13:21:18

We all know that the internet works by sending and receiving small chunks of data called packets. Back in the early days, when the internet was still in its infancy, packets were allowed to transfer freely across a connected world, however small that world was. Anyone could send packets to your system, and you could send packets to other connected systems. All services running on systems were exposed by default. As the internet started to grow, problems started to emerge, problems related to security. There are worms, viruses, unauthorized access, denial-of-service (DoS) attacks, IP spoofing, etc. Iptables is an attempt to deal with some of these problems.

Salesforce Data Cloud: Setting Up and Using the Ingestion API

Aggregated on: 2025-10-02 12:21:18

Salesforce Data Cloud offers an integrated solution for ingesting and integrating information about customers to empower businesses to provide personalized experiences at scale. At the center of the platform lies the Ingestion API, which simplifies bringing information into Data Cloud. This piece goes into the technical aspects of the Ingestion API, such as its underlying patterns, implementation, and suggestions for ingesting into Salesforce Data Cloud. Introduction to the Ingestion API The Ingestion API is a RESTful endpoint for ingesting into Salesforce Data Cloud. It provides two primary interaction patterns:

Implement a Circuit Breaker for an Unavailable API Service Running in App Connect on CP4I Using Red Hat Service Mesh

Aggregated on: 2025-10-02 11:21:18

Some applications may not respond during heavy load. These outages are due to out of memory and high CPU utilization when many requests are piled up at the application entry point since the back end is not available. In this article, we will solve this problem using Red Hat OpenShift Service Mesh, which provides a way to stop sending requests to the application or API if it is unhealthy. This article shows how to implement the Circuit Breaker pattern when an application is not responding using Red Hat OpenShift Service Mesh and IBM Cloud Pak for integration — the App Connect API service.

Bounded Rationality: Why Time-Boxed Decisions Keep Agile Teams Moving

Aggregated on: 2025-10-01 19:06:18

In Agile projects, decisions come fast and often: Which story should we pull next? Is this bug critical enough to stop the sprint? Do we ship the feature now or wait for another test cycle? Every time a Scrum team stops to analyze, debate, or wait for more data, the sprint clock keeps ticking. Delays accumulate — missed commitments, context switching, and mental fatigue ensue. Bounded Rationality, a concept introduced by Nobel laureate Herbert Simon, offers a practical response: don’t wait for perfect information. Make the best decision you can with the time and knowledge available to you.

From Keywords to Meaning: A Hands-On Tutorial With Sentence-Transformers for Semantic Search

Aggregated on: 2025-10-01 18:06:18

Traditional keyword-based search systems are inherently limited, as they operate on exact word matching rather than contextual understanding. For instance, a query such as “physician appointment” may fail to retrieve results containing “doctor visit”, despite their semantic equivalence. Recent advances in natural language processing, particularly through sentence transformers, address this gap by generating semantic embeddings — vector representations that encode meaning beyond surface-level words. These embeddings enable more sophisticated operations such as semantic similarity comparison, clustering, and context-aware retrieval.

Patterns for Building Production-Ready Multi-Agent Systems

Aggregated on: 2025-10-01 17:06:18

The Problem: When One Big Model Falls Short Imagine you’re building an AI assistant that’s supposed to handle everything: answer customer questions, do research, write code, plan schedules, all in one go. Very likely, it will start to fall apart when things get more nuanced and complex. A single model that tries to be a jack-of-all-trades often becomes master of none. And if you need to update or improve one aspect of its behavior, you’re stuck retraining or tweaking the whole giant system, which would be a maintenance nightmare. While this one-model-to-rule-them-all approach sounds tempting, it can run into practical limits. Large language models have finite context windows and sequential processing, which means that they can only consider so much information at once and handle one step at a time. For complex, open-ended problems (like researching a broad topic or managing a multi-step workflow), a single AI agent can hit a wall. Trying to cram all instructions and data into one extremely long prompt can cause confusion or omissions. In contrast, humans solve complex projects by breaking them down and delegating subtasks to specialists. Should we not apply the same principle to AI?

How Laravel Developers Handle Database Migrations Without Downtime

Aggregated on: 2025-10-01 16:06:18

Database migration is a normal, albeit crucial, task when working with Laravel applications. Database schema will be updated in this process; new columns might be added, indexes might be changed, and the table itself might be modified, all to accommodate new features or improvements in the baseline code. But database migration in a live production application is inadvisable since doing migrations can lead to application downtime, blocking tables, and possibly even worse data loss. So, can all well-experienced Laravel developers manage to upgrade the database while not interrupting the user experience? They do database migration without any downtime. In this article, we walk through the common roadblocks of database migration in Laravel, followed by the best practices and techniques implemented by developers to ensure smooth and zero-downtime migrations.

Policy-as-Code for Terraform in Regulated Environments

Aggregated on: 2025-10-01 15:06:18

Why Does It Matter? When we talk about a regulated workload, we talk about compliance. These compliances are industry standards that govern how data is processed, stored, and managed. That is why these workloads need to be clean and should be assessed based on controls we can prove. Examples of such practices are Least-Privilege access, encryption at rest, clear network boundaries, and auditability, to name a few. And then we have frameworks like NIST SP 800-53 Rev. 5, Security and Privacy Controls for Information Systems and Organizations. It provides a comprehensive set of security and privacy controls, and then we have CIS Foundations Benchmarks that translate security best practices into cloud-specific configuration checks. But none of them are enforced by themselves. But if you configure your pipeline in such a way, it can then be enforced.

Centralized vLLM on Kubernetes for Scalable LLM Infrastructure

Aggregated on: 2025-10-01 14:06:18

In the previous article, we learned how vLLM can yield dramatic performance gains by delivering 14x throughput as compared to traditional LLM serving systems. vLLM is important for efficient GPU utilization. But how can companies manage their vLLM instances across all of their production services? This would require a system that can manage the vLLM engine lifecycle and provide a way for applications to communicate with vLLM, and that can monitor, scale, and manage this setup in a production environment. This is where Kubernetes comes into the picture. Rather than treating vLLM as an individual component, companies can benefit by managing a central vLLM deployment and having the application services interact with that. In this article, we are going to explore how combining efficient GPU utilization of vLLM with a scalable and reliable orchestration platform like Kubernetes will lead to truly production-ready LLM infrastructure.

Detecting and Reducing Fake Contact Data

Aggregated on: 2025-10-01 13:06:18

Why Lead Data Quality Fails So Often In a perfect tech world, every phone number or email that flows into your product would belong to a real, reachable person. In practice, sales and marketing teams know the story is very different. Recently, two of our clients — unrelated and in different industries — came to us with a strikingly similar issue: their lead pipelines were full of contacts that looked valid but went nowhere.

Building Regulated AI Systems at Scale: Frameworks for Effective Governance

Aggregated on: 2025-10-01 12:06:18

As artificial intelligence transitions from isolated research projects to mission-critical infrastructure underpinning financial services, healthcare, and public safety systems, the governance of these systems becomes essential rather than optional. The rapid evolution of AI capabilities has outpaced our governance frameworks, creating an urgent need for architectures that balance innovation with responsibility and compliance. Financial services and payment systems provide particularly instructive lessons in building regulated AI at scale. These industries have long operated under strict regulatory requirements while still delivering innovative services to millions of customers daily. Their experience demonstrates that regulation and innovation aren't opposing forces but complementary aspects of sustainable AI deployment.

Implementing Governance on Databricks Using Unity Catalog

Aggregated on: 2025-10-01 11:06:18

Data governance has historically been the least glamorous part of data engineering. Engineers thrive on building things, designing scalable pipelines, curating high-quality datasets, and enabling machine learning models that deliver real business impact due to business demands. Governance, on the other hand, is often seen as red tape, including permissions, audit logs, compliance checks, and documentation. It doesn’t feel exciting, and it rarely gets prioritized until it’s too late. That’s why, in many organizations, governance becomes an afterthought. Teams launch pipelines into production, datasets grow, and dashboards multiply. Business users rely on the insights daily, and ML models start to influence critical decisions. But then comes the compliance request, “Who accessed customer emails last quarter?”, “Can we guarantee PII is masked in this dashboard?”, “Where did this KPI originate?” Suddenly, the lack of a centralized governance framework is exposed. Access controls are fragmented across Hive Metastore, cloud IAM, and ML registries. Lineage is incomplete, forcing engineers into manual log-diving. Masking rules are inconsistent, often implemented with brittle regex that only works for part of the data. The governance story is fragile and reactive, not proactive.

From 0.68 to 10 Requests/Second: Optimizing LLM Serving With vLLM

Aggregated on: 2025-09-30 19:21:17

GPUs are essential for running large language models (LLMs). But when companies deploy LLMs to production, having powerful GPUs alone isn't enough. What becomes equally important is handling available GPUs efficiently, so that multiple concurrent user requests can be served within sub-second response times. This requires a software layer above GPUs that can provide request batching, memory optimization, and dynamic resource management. That's exactly what vLLM strives to provide. In this article, we will explore how vLLM achieves this and examine the performance improvements it delivers.

Caching Mechanisms Using Spring Boot With Redis or AWS ElastiCache

Aggregated on: 2025-09-30 18:21:17

To decrease latency, improve responsiveness, and lessen database loads, caching has become crucial for today's performance-demanding applications. Successful caching strategies can be implemented by developers using Redis or AWS ElastiCache in conjunction with Spring Boot's elegant caching abstraction. Low-latency responses, high throughput, cost-effectiveness, and scalability are all becoming more and more important for modern applications. Effective caching can improve responsiveness to requests for recently requested data by 10–100 times, while reducing the database load by 70–90%.

Creating Real-Time Dashboards Using AWS OpenSearch, EventBridge, and WebSockets

Aggregated on: 2025-09-30 17:21:17

If you've attempted to build a dashboard, then you're familiar with the hassle of polling. You hit your API every couple of seconds, grab updates, and pray your data doesn't feel stale. However, if we're being honest, polling is inefficient, wasteful, and antiquated. In the modern era, users expect supplies to be dynamic and flowing. We, as developers, should meet that expectation without melting our servers. In this post, I will walk you through a serverless, event-driven architecture that I've leveraged to build real-time dashboards using AWS. This architecture will tie together EventBridge, OpenSearch, and API Gateway WebSockets with a hint of Lambda and DynamoDB. By the end, you'll have some understanding of how all the pieces are tied together to create a live dashboard data pipeline that can scale, can be cost-friendly, and actually feels fast for the end-user.

Building GitOps Pipelines With Helm on OpenShift: Lessons From the Trenches

Aggregated on: 2025-09-30 16:21:17

After spending the last two years knee-deep in Kubernetes deployments and watching too many "quick fixes" turn into production incidents, I've become a true believer in GitOps. Not because it's the latest buzzword, but because it actually works when you need to sleep at night. Last month, our team finally finished migrating our entire microservices platform to a GitOps workflow using Helm and OpenShift. It wasn't pretty, and we definitely learned some things the hard way. But now that the dust has settled, I wanted to share what we've discovered about making this stack work in the real world.

Experts Say This Is the Best LLM for Front-End Tasks

Aggregated on: 2025-09-30 15:21:17

Front-end development is seeing a new wave of automation thanks to large language models (LLMs). From generating UI code to reviewing pull requests, these AI models promise to speed up workflows. But which LLMs truly shine for front-end tasks? We found three experts who had shared their opinions on this topic. In this article, we will analyze their findings and opinions and try to understand which models deliver the most value when integrated into modern front-end workflows.

Scoped Filtering: A Practical Bridge to RBAC

Aggregated on: 2025-09-30 14:21:17

You’re a startup fresh out of your development-focused cycle, starting to gain traction and demo your product to potential clients. As someone working at a freshly minted Series A company, I understand the priority: get the product working. In our case, that meant demonstrating our data insights solution worked — before implementing sophisticated (but necessary) controls like role-based access control (RBAC). But now, it’s time. Clients are onboarding, and you need to ensure that only the right people can access the right customer data.

The Secret to Fast-Tracking Legacy System Modernization With GenAI

Aggregated on: 2025-09-30 13:21:17

“Generative AI is shifting from coding assistants to enterprise transformation, enabling organizations to analyze and modernize complex legacy systems.” — Gartner, Generative AI for Enterprise Transformation, 2024 Generative AI (GenAI) is often framed as a tool for accelerating developer productivity, with most discussions centering on code generation. Although that narrative captures attention, it fails to address a deeper, high-value opportunity: transforming and modernizing legacy systems. Enterprises grappling with decades-old applications can leverage GenAI not just to write code faster, but to analyze, refactor, and modernize legacy applications intelligently.

Master Advanced Error-Handling to Make PySpark Pipelines Production-Ready

Aggregated on: 2025-09-30 12:21:17

In PySpark, processing massive datasets across distributed clusters is powerful but comes with challenges. A single bad record, missing file, or network glitch can crash an entire job, wasting compute resources and leaving you with stack traces that have many lines. Spark’s lazy evaluation, where transformations don’t execute until an action is triggered, makes errors harder to catch early, and debugging them can feel like very, very difficult.

AI Risks in Product

Aggregated on: 2025-09-30 11:21:17

TL; DR: AI Risks — It’s A Trap! AI is tremendously helpful in the hands of a skilled operator. It can accelerate research, generate insights, and support better decision-making. But here’s what the AI evangelists won’t tell you: it can be equally damaging when fundamental AI risks are ignored. The main risk is a gradual transfer of product strategy from business leaders to technical systems — often without anyone deciding this should happen. Teams add “AI” and often report more output, not more learning. That pattern is consistent with long-standing human-factors findings: under time pressure, people over-trust automated cues and under-practice independent verification, which proves especially dangerous when the automation is probabilistic rather than deterministic (Parasuraman & Riley, 1997; see all sources listed below). That’s not a model failure first; it’s a system and decision-making failure that AI accelerates.

5 Manual Testing Techniques Every Tester Should Know

Aggregated on: 2025-09-29 19:06:17

Despite rapid advancements in test automation and the use of AI in software testing, manual testing is still a fundamental part of software Quality Assurance in 2025. Recent data from multiple industry reports confirm the ongoing value of manual testing in comparison to test automation. For example, only about 5% of companies perform fully automated testing, meaning all test cases are automated without manual intervention. Approximately 2/3rds of companies use a mixed approach, trying to balance both manual and automated testing efforts. Manual testing remains inevitable for the areas that require human insight, judgment, and flexibility. According to this, we may confidently say that you must have the main manual testing techniques to succeed in ensuring quality assurance on your project. So, let's walk through 5 key manual testing techniques:

How to Integrate AI APIs Into Your Projects

Aggregated on: 2025-09-29 18:06:17

Artificial intelligence isn’t just a buzzword anymore; it’s the new electricity of software development. Every other app now wants to “predict,” “recommend,” or “chat back.” But here’s the catch: integrating AI APIs can feel like wrestling an octopus. You start with excitement, then suddenly you’re buried under API keys, weird JSON outputs, and cryptic error messages. Don’t worry! You’re not alone. In this blog, we’ll break down how to integrate AI APIs into your projects without losing your sanity. We’ll cover the prep work, the integration process, best practices, and a few survival tips straight from the trenches.

A Guide to Using Browser Network Calls for Data Processing

Aggregated on: 2025-09-29 17:06:17

It was a good sunny day in Seattle, and my wife wanted to have the famous viral Dubai Chocolate Pistachio Shake. With excitement, we decided to visit the nearest Shake Shack, and to our surprise, it was sold out, and we were told to call them before visiting. There is no guarantee that it will be available the next day as well because of limited supply. Two days later, I went there again to see if there would be any, and again I was faced with disappointment. I didn't like the way, I either have to call them to check for an item or go to their store to check if it's available.

Phantom Liquidity: Why Microsecond Trades Break the Dev Simulator

Aggregated on: 2025-09-29 16:06:17

In the simulator, everything clears. The matching engine hums, the order book is balanced, and every test trader goes home happy. Then you ship it to production, and phantom liquidity vanishes faster than coffee on a trading floor. Orders that should have executed simply do not exist. The illusion is perfect until reality disagrees. I have spent enough time watching green checkmarks in dev turn into red faces in prod to know one thing: simulators lie, especially at the microsecond scale. They give you the polite version of the story — one without jitter, clock drift, or packets arriving a hair out of order. If you are lucky, you catch it in testing. If not, the market catches you.

Error Budgets 2.0 Agentic AI for SLO-Apprehensive Deployments

Aggregated on: 2025-09-29 15:06:17

Service level objectives (SLOs) and error budgets are key in site reliability engineering (SRE). They help teams balance reliability with innovation, ensuring users get a stable service while developers can safely deliver new features But in practice, administering error budgets inside CI/CD channels is hard:

Why CI and CD Should Be Treated as Separate Disciplines (Not CI/CD)

Aggregated on: 2025-09-29 14:06:17

For years, teams have bundled continuous integration (CI) and continuous delivery (CD) into a single concept: CI/CD. This shorthand suggests a seamless pipeline, but in practice, it creates confusion and hides the fact that CI and CD solve very different problems. CI is like the quality control process in a factory, meticulously inspecting and testing every component to ensure it's safe and meets standards before it's ever installed. CD, on the other hand, is the logistics company, using a deliberate strategy to deliver the finished product to the customer, monitoring its journey, and having a plan for a safe return if something goes wrong. Treating them as one often creates unoptimized workflows, blurs the separation of responsibilities, and causes confusion about what is needed when.

Build a Face-Matching ID Scanner With ReactJS and AI

Aggregated on: 2025-09-29 13:06:16

Picture this: you’re building a web app that can verify someone’s identity by having them snap a selfie with their webcam and upload a photo of their ID. It’s like something out of a sci-fi movie, but you can make it happen with ReactJS and face-api.js (a super cool library built on TensorFlow.js). This setup lets you create a working prototype in a few hours, all running right in the browser — no fancy servers required. In this guide, I’ll walk you through building a React component that compares a live webcam feed to an ID photo to confirm a match. We’ll talk about why this is awesome for quick prototyping and toss in some ideas for where you could use it.

The Serverless WebSocket: Building Real-Time Applications With Cloudflare, Hono, and Durable Objects

Aggregated on: 2025-09-29 12:06:17

The demand for real-time applications has exploded, from collaborative documents and live data dashboards to multiplayer games and instant messaging. WebSockets, with their persistent, bi-directional communication protocol, have become the de facto standard for building these experiences. However, the traditional approach — running a dedicated server to manage thousands of long-lived connections — introduces significant complexities in scalability, cost, and operational overhead. This paradigm is being fundamentally challenged by the rise of serverless computing. But can the stateless, ephemeral nature of typical serverless functions truly support a stateful, persistent protocol like WebSockets?

Better Data Beats Better Models: The Case for Data Quality in ML

Aggregated on: 2025-09-29 11:06:16

The phrase “Garbage in, Garbage out” is not a new one, and nowhere is this phrase more applicable than in machine learning. The most sophisticated and complex model architecture will crumble under the weight of poor data quality. Conversely, high-quality and reliable data can power even simple models to drive significant business impact. In this post, we will deep dive into why data quality is critical, what dimensions matter most, the problems poor data creates, and how organizations can actively monitor and improve data quality. We will also examine a practical example of credit score and close with the case for treating data quality as a first-class citizen in ML workflows.

Federated Learning: Training Models Without Sharing Raw Data

Aggregated on: 2025-09-26 19:06:15

As machine learning programs require ever-larger sets of data to train and improve, traditional central training routines creak under the burden of privacy requirements, inefficiencies in operations, and growing consumer skepticism. Liability information, such as medical records or payment history, can't easily be collected together in a place due to ethical and legal restrictions. Federated learning (FL) has a different answer. Rather than forwarding data to a model, it forwards the model to the data. Institutions and devices locally train models on their own data and forward only learned updates, not data.

Networking’s Open Source Era Is Just Getting Started

Aggregated on: 2025-09-26 18:06:15

For most of its history, networking has been a standards-first, protocol-governed domain. From the OSI model to the TCP/IP stack, progress was measured in working groups and RFCs, not GitHub commits. But that is changing fast. Projects like eBPF and Cilium, along with the architectural demands of Kubernetes, are moving networking from a specification-bound world into a software-driven, open source ecosystem. What happened to servers, developer tooling, and CI/CD pipelines is now happening to the network layer. The open source future has arrived, and it is finally catching up to the packet path.

LLM-First Vibe Coding

Aggregated on: 2025-09-26 17:06:15

For many years now, software engineers have used the Integrated Development Environment (IDE) as their main place to write and debug code. Your IDE should become a partner that helps you by predicting what you need to do, correcting mistakes automatically, and making complex code from simple prompts. "Vibe coding" is changing the field of software engineering rapidly. Its main idea is LLM-first development. Andrej Karpathy, who was Tesla's AI Director at the time, came up with the idea of vibe coding. He came up with this way of working that lets developers participate in LLM code generation [1]. The developer now needs designers to act as high-level architects who use natural language prompts to guide AI systems while they work on developing the vision for the product. Karpathy says that he builds projects and web apps by looking at them visually, giving them verbal commands, running the system, and copying code, which all lead to functional results [1]. With the traditional IDE-first development method, developers have to write every line of code. With vibe coding, on the other hand, this is not the case. Vibe coding changes software development at its core because it lets developers use AI tools to make a development environment that is completely interactive. The article shows that this trend is more than just a passing fad because it changes how software is made and kept up-to-date. Why LLM-First Development? The quick rise of LLM-first development is due to big productivity gains for developers and a complete change in the cognitive requirements of software engineering. The best thing about vibe coding is that it makes development work easier, so engineers can focus on more creative and strategic tasks.

Why One-Week Sprints Make Vibe Coding Work Better

Aggregated on: 2025-09-26 16:06:15

One-week sprint cycles in Scrum can significantly improve project outcomes through vibe coding approaches. Research shows that Agile techniques increase the success rate of projects by 21% compared to traditional methods (Ogirri & Idugie, 2024). Developers using AI assistance produce 26% more and finish 55% faster. Vibe coding maximizes the developer's flow state and focused attention, which works well with shorter cycle iterations. Teams that use one-week sprints can leverage knowing how to deliver working functionality more often. It aligns with Agile's purpose of delivering continuous value to stakeholders. Moreover, shorter sprints lower prompt drift and allow quicker verification of features developed by AI. Product managers and entrepreneurs who utilize Lean practices may see their 'build, measure, learn' loop accelerated by incorporating vibe coding into Agile development. Cross-functional teams use this potent combination to develop functional prototypes, as detailed coding experience or significant investment is no longer required.

Complex Data Tasks Are Now One-Liners With AI in Databricks SQL

Aggregated on: 2025-09-26 15:06:15

As data engineers, we’ve all encountered those recurring requests from business stakeholders: “Can you summarize all this text into something executives can read quickly?”, “Can we translate customer reviews into English so everyone can analyze them?”, or “Can we measure customer sentiment at scale without building a new pipeline?”. Traditionally, delivering these capabilities required a lot of heavy lifting. You’d have to export raw data from the warehouse into a Python notebook, clean and preprocess it, connect to an external NLP API or host your own machine learning model, handle retries, manage costs, and then write another job to push the results back into a Delta table. The process was brittle, required multiple moving parts, and — most importantly — took the analysis out of the governed environment, creating compliance and reproducibility risks. With the introduction of AI functions in Databricks SQL, that complexity is abstracted away. Summarization, translation, sentiment detection, document parsing, masking, and even semantic search can now be expressed in one-line SQL functions, running directly against governed data. There’s no need for additional infrastructure, no external services to maintain, and no custom ML deployments to babysit. Just SQL, governed and scalable, inside the Lakehouse.

Basic Security Setup for Startups

Aggregated on: 2025-09-26 14:06:15

Preamble I recently had a conversation with my friend about starting a new company. We discussed the various stages a company should go through to become mature and secure enough to operate in the modern market. This article will outline those stages. The suggested approach is based on the following principles: Security by default Security by design Identification, authentication, and authorization Segregation of responsibilities You can follow this flow assuming that you're starting a product from scratch without any existing VNETs, IDPs, or parent companies' networks. However, if you have any of these things, you must adjust the flow accordingly.

Implementing a Multi-Agent KYC System

Aggregated on: 2025-09-26 13:06:15

Every engineer who implemented KYC systems has dealt with a frustrating reality. You build rule-based engines that break every time regulations change. Document processing takes days because everything goes through manual review queues. API integrations become brittle nightmares when you're trying to coordinate identity verification, OCR services, and watchlist screening. The numbers tell the story: most KYC systems process documents in 2–3 days with false positive rates hitting 15-20%. That means one in five legitimate customers gets flagged for manual review. Meanwhile, compliance teams burn out reviewing thousands of documents daily, and customer support fields endless calls about delayed approvals.

Building a Real-Time Data Mesh With Apache Iceberg and Flink

Aggregated on: 2025-09-26 12:06:15

If you’ve ever tried to scale your organization’s data infrastructure beyond a few teams, you know how fast a carefully planned “data lake” can degenerate into an unruly “data swamp.” Pipelines are pushing files nonstop, tables sprout like mushrooms after a rainy day, and no one is quite sure who owns which dataset. Meanwhile, your real-time consumers are impatient for fresh data, your batch pipelines crumble on every schema change, and governance is an afterthought at best. At that point, someone in a meeting inevitably utters the magic word: data mesh. Decentralized data ownership, domain-oriented pipelines, and self-service access all sound perfect on paper. But in practice, it can feel like you’re trying to build an interstate highway system while traffic is already barreling down dirt roads at full speed.

AI Transformation Déjà Vu

Aggregated on: 2025-09-26 11:06:15

TL;DR: AI Transformation Failures Organizations seem to fail their AI transformation using the same patterns that killed their Agile transformations: Performing demos instead of solving problems, buying tools before identifying needs, celebrating pilots that can’t scale, and measuring activity instead of outcomes. These aren’t technology failures; they are organizational patterns of performing change instead of actually changing. Your advantage isn’t AI expertise; it’s pattern recognition from surviving Agile. Use it to spot theater, demand real problems before tools, insist on integration from day one, and measure actual value delivered.

Implementing Vector Search in Databricks

Aggregated on: 2025-09-25 19:22:30

Search has always been at the heart of analytics. Whether you’re tracking down the right transaction, filtering a customer record, or pulling a specific review, the default approach has traditionally been keyword search. Keyword search is simple and effective when you know exactly what you’re looking for, but it quickly falls apart when the language is messy, ambiguous, or when meaning matters more than exact words. That’s where vector search changes the game. Instead of matching literal keywords, vector search relies on embeddings — high-dimensional numeric representations of text, images, or other unstructured content — that capture semantic meaning.

The GPT-5 Impact

Aggregated on: 2025-09-25 18:22:30

ChatGPT happened. A host of models happened. Improvements continue to come out at an accelerated pace. The focus of this small article is to see if we can keep pace with our designs and remain both efficient and relevant to the latest and greatest. I don't have a host of Elo benchmarks and ratings to evaluate these models. All I have is a small design for solving Math and Science problems that has generally kept me honest and grounded, whether it was using Cursor or Windsurf, or lately, GitHub CoPilot to write code, or in the choice of models (GPT-4o was clearly my favorite up until today!).

Boosting Developer Productivity in Kubernetes-Driven Workflows: A Practical Checklist

Aggregated on: 2025-09-25 17:22:30

Editor's Note: The following is an article written for and published in DZone's 2025 Trend Report, Kubernetes in the Enterprise: Optimizing the Scale, Speed, and Intelligence of Cloud Operations. Kubernetes has become the backbone of application deployment. Its flexibility and scalability are long-time proven, but its adoption by developers can still be a challenge. The misuse of Kubernetes configuration, through the thousands of options, can make applications less performant or less resilient in that they would be a single old-school server. To fully take advantage of Kubernetes, organizations must prioritize the developer experience by embracing platform engineering practices that abstract complexity and provide self-service capabilities, enabling teams to deploy applications with confidence.

AI-Powered Triathlon Coaching: Building a Modern Training Assistant With Claude and Garmin

Aggregated on: 2025-09-25 16:22:30

The Triathlon Training Challenge Triathlon is arguably one of the most complex sports to train for. Unlike single-discipline sports, triathletes must master three distinct activities — swimming, cycling, and running — while managing the intricate balance between them. The challenge isn’t just about getting better at each sport; it’s about understanding how training in one affects the others, managing fatigue across disciplines, and walking a razor-thin line between optimal training and injury. The modern triathlete faces an overwhelming array of variables. How many hours per week should you train? What distribution across sports? When do you push hard, and how much recovery do you need between sessions? Add in technique refinement, physiological monitoring, equipment optimization, nutrition periodization, and injury prevention, and you have a sport where the “art of training” has evolved into a complex science requiring constant analysis and adjustment.

The Design System Team: Goals, Pains, and Successes

Aggregated on: 2025-09-25 15:22:30

A design system is a collection of reusable components, guidelines, patterns, and best practices (including accessibility and responsiveness) that help a company build consistent and efficient user interfaces. It provides the building blocks to create a cohesive user experience across your product or products and platforms. Multiple disciplines are involved: design, front-end engineering, product management, and more. A design system team is a group of people who cover the disciplines mentioned and who are responsible for the design system.